Combination of spatio-temporal correction methods using traffic survey data for reconstruction of people flow

Data on people flow has become increasingly important in various fields, including marketing and public services. Although mobile phones enable the user's position to be located with a certain degree of accuracy from a large number of people and become one of the most promising devise, unwillingness to share related with privacy issues still remain. Therefore, it is also important to establish a practical method for reconstructing people flow from various kinds of existing fragmentary spatio-temporal data, such as public traffic survey data, from a view of complementariness with mobile phone data. In this study, we propose a combination of spatio-temporal correction processes to a previously published method, to generate continuous spatio-temporal people flow data sets at chosen intervals in selected cities. The correction methods include temporal smoothing of departure time using kernel density estimation, network data correction in OpenStreetMap data, and spatial smoothing in geocoding with MODIS data. We also compare the reconstruction accuracy by deriving correlation coefficients for different combinations of correction methods. Such reconstructed people flow data can potentially be used as infrastructure data in various fields, including emergency planning and related events in areas where data collection and real-time awareness are weak.